PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.
This research explores how changes in groundwater levels affect the chemistry of underground water, especially in areas where land meets water, like wetlands.
This study provides a comprehensive analysis of isolated deep convection & mesoscale convective systems using self-organizing maps to categorize large-scale meteorological patterns and a tracking algorithm to monitor their life cycle.
This study explored the future effects of climate change and low-carbon energy transition (i.e., emission reduction) on Arctic offshore oil and gas production.
Hydropower could expand substantially during the 21st century in many regions of the world to meet rising or changing energy demands. However, this expansion might harm river ecosystems.
Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.
This work shows that linear pattern scaling is an effective means of obtaining global-to-local relationships for CMIP6 models, as it has been in past model eras.
A switchable single-atom catalyst is activated in the presence of surface intermediates and reverts to its stable inactive form when the reaction is completed.
This study examined the role of river sinuosity using computer models to understand what drives hyporheic exchange, a process that significantly affects water quality and ecosystem health.
Skillful predictions of tropical cyclone activity on subseasonal time scales may help mitigate their destructive impacts. This study investigates the combined impacts of atmospheric phenomena to better understand cyclone activity.
In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.
Topographic variations have substantial impacts on surface hydrologic processes. This study introduced a new subgrid structure and methods to increase model accuracy for snow water equivalent predictions.
